A New Approach of Neurofuzzy Learning Algorithm

Masaharu Mizumoto and Yan Shi
Additional contact information
Masaharu Mizumoto: Osaka Electro-Communication University, Division of Information and Computer Sciences
Yan Shi: Osaka Electro-Communication University, Division of Information and Computer Sciences

Chapter 5 in Intelligent Hybrid Systems, 1997, pp 109-129 from Springer

Abstract: Abstract In this chapter, we develop a new approach of neurofuzzy learning algorithm for tuning fuzzy rules by using training input-output data, based on the gradient descent method. The major advantage of this approach is that fuzzy rules or membership functions can be learned without changing the form of fuzzy rule table used in usual fuzzy applications, so that the case of weak-firing can be well avoided, which is different from the conventional neurofuzzy learning algorithms. Moreover, we show the efficiency of the developed method by identifying nonlinear functions.

Date: 1997
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-6191-0_5

Ordering information: This item can be ordered from
http://www.springer.com/9781461561910

DOI: 10.1007/978-1-4615-6191-0_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().